When you dictate a message, you give a tool access to a thought before you have finished shaping it. When you record a meeting, you capture the discussion behind a decision: the hesitation, the disagreement, the promise someone made.
Voice makes it easier to get knowledge out of your head. It also makes the question of where that knowledge goes much more important.
If transcription happens in the cloud, your audio goes to a provider for processing. If the resulting notes live inside a hosted workspace, your access depends on that service’s rules.
Before you build years of work there, ask a simple question: if you stop paying tomorrow, what can you still open?
Your work should remain useful after your relationship with a tool ends.
The value is in what you remember
Imagine asking an agent to draft a customer proposal.
It knows your product and can write a convincing document. But does it know that you promised this customer a smaller rollout? That their team cannot adopt another dashboard? That the last proposal failed because implementation required too much work?
Those details change the answer.
They accumulate through meetings, messages, notes, corrections, and experience. Together, they explain how your work actually happens.
You might use different agents for research, writing, and development. Each can do a better job when it has access to the relevant parts of that history. A decision captured today can improve a proposal next week and a product specification next month.
That is how context compounds: you preserve what you learn, then put it to work again.
The harder kind of dependency
Changing models is relatively straightforward when your knowledge exists independently of them.
It becomes harder when one service holds the only useful account of your projects: what happened, why it happened, what remains unresolved, and who is responsible.
Even an export may leave you with work to do. A pile of conversations is different from a usable record of decisions, with dates and links to the original sources.
The cost of leaving becomes the effort of reconstructing that understanding.
That also weakens your ability to respond to pricing changes. With usage-based AI, more activity can mean a larger bill. Paying for useful computation is reasonable. Feeling unable to leave because your working history is trapped there is a different bargain.
The intelligence you choose and the knowledge you keep should be separate architectural decisions.
What ownership should mean
Ownership needs to show up in everyday use.
You should be able to read your work outside the app that created it. Move it to another tool. Back it up. Correct an outdated assumption. See where a conclusion came from. Decide which parts an agent can access.
For a team, shared context also needs boundaries. A useful company memory does not require every assistant to receive every conversation.
And keeping more information is not automatically better. Context becomes valuable when it is accurate, relevant, and maintained. Saving the reason for a decision may matter more than saving another thousand lines of chat.
Why we’re building Cue local-first
This is the thinking behind Cue’s architecture.
The foundation is work you can keep. Cue stores notes as ordinary Markdown files in a folder on your Mac. You can open them in another editor, move them, and keep reading them without Cue or a paid plan. How Cue stores your work.
The same principle applies when you speak. Cue offers on-device dictation, which works offline after the required model is downloaded, alongside optional cloud transcription. You can choose where speech processing happens. Dictation in Cue.
Your context can also be available to compatible agents through MCP, a standard for connecting AI tools to other systems. You initiate the connection and grant access to the notes and meetings in your Cue folder. Connecting your agents.
Those choices have distinct boundaries. Local files do not make a cloud request local: cloud transcription sends audio to a provider, and cloud-backed AI sends the input and context needed for the request. The receiving service’s data practices still apply.
Local-first gives the work a home you control, with deliberate choices about what you connect.
The principle must apply to Cue, too. If another tool suits you better, your notes should still be yours to use.
We want Cue to earn its place in your workflow through the help it provides. The knowledge you build along the way should remain yours—ready for whichever intelligence you choose next.
Product details checked September 30, 2026, against Cue’s FAQ, dictation, and MCP documentation.
Start with the guide to free voice-to-text for Mac, or download Cue.